Study of spectral overlap and heterogeneity in agriculture based on soft classification techniques

Abstract

This study explores the application of fuzzy soft classification techniques combined with vegetation indices to address spectral overlap and heterogeneity in agricultural image processing. The methodology focuses on the integration of three key vegetation indices: Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Modified Chlorophyll Absorption in Reflectance Index (MCARI), with Modified Possibilistic C-Means (MPCM) clustering. The analysis involves preprocessing the image data, calculating the vegetation indices, and applying the MPCM algorithm to perform soft classification, allowing pixels to belong to multiple classes with varying degrees of membership. A quantitative assessment is conducted to evaluate the accuracy of the classification results. Methodological approach:

• •
  Integrating advanced image processing techniques and vegetative band ratios with the fuzzy classification method MPCM to handle the inherent complexities in agricultural image analysis, such as spectral overlap and mixed boundaries.
• •
  Quantitative assessment of classification accuracy using Fuzzy Error Matrices (FERM).

This approach provides a robust framework for analyzing spectral overlaps among the crops and weeds and improving the accuracy of crop classification, particularly in heterogeneous environments.